"""Base classes for FastMCP prompts."""

from __future__ import annotations as _annotations

import warnings
from collections.abc import Callable
from typing import TYPE_CHECKING, Any, ClassVar, Literal, overload

import pydantic
import pydantic_core

if TYPE_CHECKING:
    from docket import Docket
    from docket.execution import Execution

    from fastmcp.prompts.function_prompt import FunctionPrompt
import mcp.types
from mcp import GetPromptResult
from mcp.types import (
    EmbeddedResource,
    Icon,
    PromptMessage,
    TextContent,
)
from mcp.types import Prompt as SDKPrompt
from mcp.types import PromptArgument as SDKPromptArgument
from pydantic import Field
from pydantic.json_schema import SkipJsonSchema

from fastmcp.server.auth.authorization import AuthCheck
from fastmcp.server.tasks.config import TaskConfig, TaskMeta
from fastmcp.utilities.components import FastMCPComponent
from fastmcp.utilities.logging import get_logger
from fastmcp.utilities.types import (
    FastMCPBaseModel,
)

logger = get_logger(__name__)


class Message(pydantic.BaseModel):
    """Wrapper for prompt message with auto-serialization.

    Accepts any content - strings pass through, other types
    (dict, list, BaseModel) are JSON-serialized to text.

    Example:
        ```python
        from fastmcp.prompts import Message

        # String content (user role by default)
        Message("Hello, world!")

        # Explicit role
        Message("I can help with that.", role="assistant")

        # Auto-serialized to JSON
        Message({"key": "value"})
        Message(["item1", "item2"])
        ```
    """

    role: Literal["user", "assistant"]
    content: TextContent | EmbeddedResource

    def __init__(
        self,
        content: Any,
        role: Literal["user", "assistant"] = "user",
    ):
        """Create Message with automatic serialization.

        Args:
            content: The message content. str passes through directly.
                     TextContent and EmbeddedResource pass through.
                     Other types (dict, list, BaseModel) are JSON-serialized.
            role: The message role, either "user" or "assistant".
        """
        # Handle already-wrapped content types
        if isinstance(content, (TextContent, EmbeddedResource)):
            normalized_content: TextContent | EmbeddedResource = content
        elif isinstance(content, str):
            normalized_content = TextContent(type="text", text=content)
        else:
            # dict, list, BaseModel → JSON string
            serialized = pydantic_core.to_json(content, fallback=str).decode()
            normalized_content = TextContent(type="text", text=serialized)

        super().__init__(role=role, content=normalized_content)

    def to_mcp_prompt_message(self) -> PromptMessage:
        """Convert to MCP PromptMessage."""
        return PromptMessage(role=self.role, content=self.content)


class PromptArgument(FastMCPBaseModel):
    """An argument that can be passed to a prompt."""

    name: str = Field(description="Name of the argument")
    description: str | None = Field(
        default=None, description="Description of what the argument does"
    )
    required: bool = Field(
        default=False, description="Whether the argument is required"
    )


class PromptResult(pydantic.BaseModel):
    """Canonical result type for prompt rendering.

    Provides explicit control over prompt responses: multiple messages,
    roles, and metadata at both the message and result level.

    Accepts:
        - str: Wrapped as single Message (user role)
        - list[Message]: Used directly for multiple messages or custom roles

    Example:
        ```python
        from fastmcp import FastMCP
        from fastmcp.prompts import PromptResult, Message

        mcp = FastMCP()

        # Simple string content
        @mcp.prompt
        def greet() -> PromptResult:
            return PromptResult("Hello!")

        # Multiple messages with roles
        @mcp.prompt
        def conversation() -> PromptResult:
            return PromptResult([
                Message("What's the weather?"),
                Message("It's sunny today.", role="assistant"),
            ])
        ```
    """

    messages: list[Message]
    description: str | None = None
    meta: dict[str, Any] | None = None

    def __init__(
        self,
        messages: str | list[Message],
        description: str | None = None,
        meta: dict[str, Any] | None = None,
    ):
        """Create PromptResult.

        Args:
            messages: String or list of Message objects.
            description: Optional description of the prompt result.
            meta: Optional metadata about the prompt result.
        """
        normalized = self._normalize_messages(messages)
        super().__init__(messages=normalized, description=description, meta=meta)

    @staticmethod
    def _normalize_messages(
        messages: str | list[Message],
    ) -> list[Message]:
        """Normalize input to list[Message]."""
        if isinstance(messages, str):
            return [Message(messages)]
        if isinstance(messages, list):
            # Validate all items are Message
            for i, item in enumerate(messages):
                if not isinstance(item, Message):
                    raise TypeError(
                        f"messages[{i}] must be Message, got {type(item).__name__}. "
                        f"Use Message({item!r}) to wrap the value."
                    )
            return messages
        raise TypeError(
            f"messages must be str or list[Message], got {type(messages).__name__}"
        )

    def to_mcp_prompt_result(self) -> GetPromptResult:
        """Convert to MCP GetPromptResult."""
        mcp_messages = [m.to_mcp_prompt_message() for m in self.messages]
        return GetPromptResult(
            description=self.description,
            messages=mcp_messages,
            _meta=self.meta,  # type: ignore[call-arg]  # _meta is Pydantic alias for meta field
        )


class Prompt(FastMCPComponent):
    """A prompt template that can be rendered with parameters."""

    KEY_PREFIX: ClassVar[str] = "prompt"

    arguments: list[PromptArgument] | None = Field(
        default=None, description="Arguments that can be passed to the prompt"
    )
    auth: SkipJsonSchema[AuthCheck | list[AuthCheck] | None] = Field(
        default=None, description="Authorization checks for this prompt", exclude=True
    )

    def to_mcp_prompt(
        self,
        **overrides: Any,
    ) -> SDKPrompt:
        """Convert the prompt to an MCP prompt."""
        arguments = [
            SDKPromptArgument(
                name=arg.name,
                description=arg.description,
                required=arg.required,
            )
            for arg in self.arguments or []
        ]

        return SDKPrompt(
            name=overrides.get("name", self.name),
            description=overrides.get("description", self.description),
            arguments=arguments,
            title=overrides.get("title", self.title),
            icons=overrides.get("icons", self.icons),
            _meta=overrides.get(  # type: ignore[call-arg]  # _meta is Pydantic alias for meta field
                "_meta", self.get_meta()
            ),
        )

    @classmethod
    def from_function(
        cls,
        fn: Callable[..., Any],
        *,
        name: str | None = None,
        version: str | int | None = None,
        title: str | None = None,
        description: str | None = None,
        icons: list[Icon] | None = None,
        tags: set[str] | None = None,
        meta: dict[str, Any] | None = None,
        task: bool | TaskConfig | None = None,
        auth: AuthCheck | list[AuthCheck] | None = None,
    ) -> FunctionPrompt:
        """Create a Prompt from a function.

        The function can return:
        - str: wrapped as single user Message
        - list[Message | str]: converted to list[Message]
        - PromptResult: used directly
        """
        from fastmcp.prompts.function_prompt import FunctionPrompt

        return FunctionPrompt.from_function(
            fn=fn,
            name=name,
            version=version,
            title=title,
            description=description,
            icons=icons,
            tags=tags,
            meta=meta,
            task=task,
            auth=auth,
        )

    async def render(
        self,
        arguments: dict[str, Any] | None = None,
    ) -> str | list[Message | str] | PromptResult:
        """Render the prompt with arguments.

        Subclasses must implement this method. Return one of:
        - str: Wrapped as single user Message
        - list[Message | str]: Converted to list[Message]
        - PromptResult: Used directly
        """
        raise NotImplementedError("Subclasses must implement render()")

    def convert_result(self, raw_value: Any) -> PromptResult:
        """Convert a raw return value to PromptResult.

        Accepts:
            - PromptResult: passed through
            - str: wrapped as single Message
            - list[Message | str]: converted to list[Message]

        Raises:
            TypeError: for unsupported types
        """
        if isinstance(raw_value, PromptResult):
            return raw_value

        if isinstance(raw_value, str):
            return PromptResult(raw_value, description=self.description, meta=self.meta)

        if isinstance(raw_value, list | tuple):
            messages: list[Message] = []
            for i, item in enumerate(raw_value):
                if isinstance(item, Message):
                    messages.append(item)
                elif isinstance(item, str):
                    messages.append(Message(item))
                else:
                    raise TypeError(
                        f"messages[{i}] must be Message or str, got {type(item).__name__}. "
                        f"Use Message({item!r}) to wrap the value."
                    )
            return PromptResult(messages, description=self.description, meta=self.meta)

        raise TypeError(
            f"Prompt must return str, list[Message], or PromptResult, "
            f"got {type(raw_value).__name__}"
        )

    @overload
    async def _render(
        self,
        arguments: dict[str, Any] | None = None,
        task_meta: None = None,
    ) -> PromptResult: ...

    @overload
    async def _render(
        self,
        arguments: dict[str, Any] | None,
        task_meta: TaskMeta,
    ) -> mcp.types.CreateTaskResult: ...

    async def _render(
        self,
        arguments: dict[str, Any] | None = None,
        task_meta: TaskMeta | None = None,
    ) -> PromptResult | mcp.types.CreateTaskResult:
        """Server entry point that handles task routing.

        This allows ANY Prompt subclass to support background execution by setting
        task_config.mode to "supported" or "required". The server calls this
        method instead of render() directly.

        Args:
            arguments: Prompt arguments
            task_meta: If provided, execute as background task and return
                CreateTaskResult. If None (default), execute synchronously and
                return PromptResult.

        Returns:
            PromptResult when task_meta is None.
            CreateTaskResult when task_meta is provided.

        Subclasses can override this to customize task routing behavior.
        For example, FastMCPProviderPrompt overrides to delegate to child
        middleware without submitting to Docket.
        """
        from fastmcp.server.tasks.routing import check_background_task

        task_result = await check_background_task(
            component=self,
            task_type="prompt",
            arguments=arguments,
            task_meta=task_meta,
        )
        if task_result:
            return task_result

        # Synchronous execution
        result = await self.render(arguments)
        return self.convert_result(result)

    def register_with_docket(self, docket: Docket) -> None:
        """Register this prompt with docket for background execution."""
        if not self.task_config.supports_tasks():
            return
        docket.register(self.render, names=[self.key])

    async def add_to_docket(  # type: ignore[override]
        self,
        docket: Docket,
        arguments: dict[str, Any] | None,
        *,
        fn_key: str | None = None,
        task_key: str | None = None,
        **kwargs: Any,
    ) -> Execution:
        """Schedule this prompt for background execution via docket.

        Args:
            docket: The Docket instance
            arguments: Prompt arguments
            fn_key: Function lookup key in Docket registry (defaults to self.key)
            task_key: Redis storage key for the result
            **kwargs: Additional kwargs passed to docket.add()
        """
        lookup_key = fn_key or self.key
        if task_key:
            kwargs["key"] = task_key
        return await docket.add(lookup_key, **kwargs)(arguments)

    def get_span_attributes(self) -> dict[str, Any]:
        return super().get_span_attributes() | {
            "fastmcp.component.type": "prompt",
            "fastmcp.provider.type": "LocalProvider",
        }


__all__ = [
    "Message",
    "Prompt",
    "PromptArgument",
    "PromptResult",
]


def __getattr__(name: str) -> Any:
    """Deprecated re-exports for backwards compatibility."""
    deprecated_exports = {
        "FunctionPrompt": "FunctionPrompt",
        "prompt": "prompt",
    }

    if name in deprecated_exports:
        import fastmcp

        if fastmcp.settings.deprecation_warnings:
            warnings.warn(
                f"Importing {name} from fastmcp.prompts.prompt is deprecated. "
                f"Import from fastmcp.prompts.function_prompt instead.",
                DeprecationWarning,
                stacklevel=2,
            )
        from fastmcp.prompts import function_prompt

        return getattr(function_prompt, name)

    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")